name: vertex-tuning-gemini description: > Vertex AI Gemini Model Tuning. Use when you need to fine-tune Gemini models using Vertex AI's infrastructure.

Vertex AI Gemini Model Tuning

Overview

This skill provides procedural knowledge for fine-tuning Gemini Large Language Models using Vertex AI's tuning service. It covers the entire lifecycle from environment setup and data preparation to job configuration, monitoring, and deployment.

Workflow Decision Tree

1. Environment Check: Has the environment (Auth, APIs, IAM, Venv) been initialized?

   • No → Go to Phase 0: Environment & IAM Setup.
   • Yes → Proceed.

2. Dataset Status: Is the dataset ready in JSONL format and uploaded to GCS?

   • No → Go to Phase 1: Dataset Preparation & Upload.
   • Yes → Proceed.

3. Configuration: Have the target Gemini model and hyperparameters been decided?

   • No → Go to Phase 2: Model Configuration & Recommendation.
   • Yes → Proceed.

4. Job Status: Has the tuning job been submitted?

   • No → Go to Phase 3: Tuning Job Execution.
   • Yes → Proceed.

5. Job Completion: Is the tuning job complete?

   • No → Go to Phase 4: Monitoring.
   • Yes → Proceed.

6. Deployment: Has the tuned model been deployed (if required)?

   • No → Go to Phase 5: Model Deployment.
   • Yes → Task Complete.

Phase 0: Environment & IAM Setup {#phase-0}

Ensure the foundational environment is ready before proceeding.

0.1 Authentication & Project Context

• Check if gcloud CLI is installed. If it is not installed, prompt the user for permission to install it before proceeding.
• Verify gcloud auth list. If not authenticated, run gcloud auth login.
• Ensure project and location are known. Use gcloud config get project to retrieve the current project (and gcloud config get compute/region for region).
• CRITICAL: Ask for Confirmation. You must prompt the user to confirm the retrieved project and region before proceeding, in case they want to switch to a different one.

0.2 Enable APIs

Ensure aiplatform.googleapis.com and storage.googleapis.com are enabled.

gcloud services enable aiplatform.googleapis.com storage.googleapis.com --project=YOUR_PROJECT

0.3 IAM Permissions

Verify the following identities have the required roles.

• Vertex AI Service Agent: service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com
• Managed OSS Fine Tuning Service Agent: service-PROJECT_NUMBER@gcp-sa-vertex-moss-ft.iam.gserviceaccount.com
• User Identity: The account running the commands.

0.4 Virtual Environment

Create and use a virtual environment named tuning_agent_venv in the home directory. Install dependencies from references/requirements.txt.

python3 -m venv ~/tuning_agent_venv
source ~/tuning_agent_venv/bin/activate
pip install -r references/requirements.txt

Phase 1: Dataset Preparation & Upload {#phase-1}

Vertex AI requires valid JSONL format in GCS.

1.0 Dataset Discovery & Confirmation

• Ask the User First: Ask the user if they already have a dataset they want to use.
• Auto-Discovery: If the user does not have a dataset, search the authenticated project's GCS buckets to find if any existing file has a reasonable dataset that can do the job the user prompted initially.
• CRITICAL: Ask for Confirmation. Do not proceed with dataset preparation or upload until you present the found or provided dataset to the user and they confirm the dataset to use.

1.1 Formatting & Validation

• Conversion: If data is in CSV or JSON, use scripts/prepare_dataset.py to convert.
• Validation: If data is already in JSONL, validate it before uploading: bash python3 scripts/prepare_dataset.py \ --input my_data.jsonl \ --format messages_gemini \ --validate_only
• Refer to Data Preparation Guide for required schemas.

1.2 Upload

Upload formatted .jsonl files to GCS using a unique directory (e.g., with a datetime timestamp) to avoid overwriting outputs from different runs.

gcloud storage cp dataset.jsonl gs://YOUR_BUCKET/tuning_agent_job_<datetime>/dataset.jsonl

Phase 2: Model Configuration & Recommendation {#phase-2}

Help the user choose the best Gemini model and parameters. Always seek user confirmation before submitting the job.

• If the user does not specify a specific model in their prompt, calculate recommendations based on the Models Catalog.
• Prompt for Confirmation: Present the recommended model to the user and ask for their confirmation before configuring hyperparameters.

2.1 Configuration

• Hyperparameter recommendation guidelines and options for Gemini models will be populated here.
• Prompt for Confirmation: Present the proposed configuration to the user and ask for their approval before proceeding.

Phase 3: Tuning Job Execution {#phase-3-tuning-job-execution}

Submit the Gemini model tuning job using scripts/tune_gemini_model.py.

python3 scripts/tune_gemini_model.py

Phase 4: Monitoring {#phase-4-monitoring}

Monitor the job via the Cloud Console link provided in the script output or by polling the job status.

Phase 5: Model Deployment {#phase-5-model-deployment}

Once the Gemini model tuning job is SUCCEEDED, deploy the model using scripts/deploy_gemini_model.py.

python3 scripts/deploy_gemini_model.py

Resources

• Data Preparation Guide
• Models Catalog
• Tuning Guide
• scripts/prepare_dataset.py: Data conversion & validation.
• scripts/tune_gemini_model.py: Gemini model tuning job submission.
• scripts/deploy_gemini_model.py: Gemini model deployment.